Damage Prediction Techniques for Structural Health Monitoring in Bridge using sensors and ANN-Machine Learning Technique

Abstract

In the last few decades, structural health monitoring(SHM) has been a major concern, and in the study of data collected from monitoring devices embedded in the networks, it gives engineers deep understanding about the failure of civilian infrastructure. Commonly named structural health monitoring is the method of applying a damage recognition for climate, civil and mechanical engineering facilities With both the growth of technologies sensor networks (SNs) a vast number of sensors is fitted with architectural or mechanical systems to receive real-time information on their wellbeing, suggesting that data handling in WSN-based SHM is of significant importance. Increased SHM innovation has been empowered with the development of intelligent sensors and real-time connectivity systems over wireless sensor networks (WSN). Recently, predictive time series simulations for structural damage detection due to the function coefficients resistance and unresolved structural damage mistakes have been commonly used. Machine Learning algorithms (ML) are progressively used to predict damage. The main approach is the tool used to estimate the degree of damaged brides via the sensors. In the second step artificial neural network (ANN) method enabling the detection level objectively describes the generalization error of each bride.